!pip install prophet

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import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
import warnings
import folium
from folium.plugins import HeatMap, HeatMapWithTime
from IPython.display import display, HTML
from scipy.stats import percentileofscore
from sklearn.metrics import mean_absolute_error, mean_squared_error
from prophet import Prophet
import plotly.express as px
# import requests

# Ignore all warnings
warnings.filterwarnings('ignore')
# Adjust display options for pandas
pd.set_option('display.max_columns', 100)  # Show all columns
# Setting a professional style
sns.set_theme(style="dark")

from google.colab import output
output.enable_custom_widget_manager()
# output.disable_custom_widget_manager()

def describe_data(df):
    summary = {}

    for col in df.columns:
        if pd.api.types.is_numeric_dtype(df[col]):  # Continuous variables
            summary[col] = {
                'Type': 'Continuous',
                'Count': df[col].count(),
                'Mean': df[col].mean(),
                'Median': df[col].median(),
                'Std': df[col].std(),
                'Min': df[col].min(),
                '25%': df[col].quantile(0.25),
                '50%': df[col].quantile(0.50),
                '75%': df[col].quantile(0.75),
                'Max': df[col].max(),
            }
        elif pd.api.types.is_categorical_dtype(df[col]) or df[col].dtype == object:  # Categorical variables
            summary[col] = {
                'Type': 'Categorical',
                'Count': df[col].count(),
                'Unique': df[col].nunique(),
                'Top': df[col].mode()[0] if not df[col].mode().empty else None,
                'Freq': df[col].value_counts().iloc[0] if not df[col].value_counts().empty else None
            }

    return pd.DataFrame(summary).T

citibike_trip_sample_df = pd.read_csv('data/citibike-trips-sample.csv')
citibike_trip_sample_df['starttime'] = pd.to_datetime(citibike_trip_sample_df['starttime'])
citibike_trip_sample_df['start_hour'] = citibike_trip_sample_df['starttime'].dt.hour
citibike_trip_sample_df['start_month'] = citibike_trip_sample_df['starttime'].dt.month
citibike_trip_sample_df['start_year'] = citibike_trip_sample_df['starttime'].dt.year

citibike_trip_sample_df['stoptime'] = pd.to_datetime(citibike_trip_sample_df['stoptime'])
citibike_trip_sample_df['stop_hour'] = citibike_trip_sample_df['stoptime'].dt.hour
citibike_trip_sample_df['stop_month'] = citibike_trip_sample_df['stoptime'].dt.month
citibike_trip_sample_df['stop_year'] = citibike_trip_sample_df['stoptime'].dt.year
citibike_trip_sample_df[['start_station_id', 'end_station_id']] = citibike_trip_sample_df[['start_station_id', 'end_station_id']].astype(str)
citibike_trip_sample_df['user_age'] = citibike_trip_sample_df['start_year'] - citibike_trip_sample_df['birth_year']
citibike_trip_sample_df.shape

(315785, 23)

# Check the time range of the trip sample data
print("Start Time Range: {} to {}".format(citibike_trip_sample_df.starttime.min(), citibike_trip_sample_df.starttime.max()))
print("Stop Time Range: {} to {}".format(citibike_trip_sample_df.stoptime.min(), citibike_trip_sample_df.stoptime.max()))

Start Time Range: 2013-07-01 00:02:00 to 2016-08-30 23:33:00
Stop Time Range: 2013-07-01 00:12:00 to 2016-08-30 23:50:00

citibike_trip_sample_df.head()

describe_data(citibike_trip_sample_df)

citibike_station_df = pd.read_csv('data/citibike-stations.csv')
citibike_station_df[['station_id_int', 'region_id', 'eightd_has_key_dispenser', 'is_installed', 'is_renting', 'is_returning', 'eightd_has_available_keys']] = citibike_station_df[['station_id_int', 'region_id', 'eightd_has_key_dispenser', 'is_installed', 'is_renting', 'is_returning', 'eightd_has_available_keys']].astype(str)
citibike_station_df.shape

(1802, 17)

citibike_station_df.head()

# citibike_station_df.region_id.value_counts()

describe_data(citibike_station_df)

# Find out start time is later than stop time
# citibike_trip_sample_df.query('starttime>=stoptime').shape
citibike_trip_sample_df.query('starttime>=stoptime')

plt.figure(figsize=(10, 6))
sns.histplot(citibike_trip_sample_df[citibike_trip_sample_df['user_age'] <
                                     citibike_trip_sample_df['user_age'].quantile(0.999)],
             x='user_age', kde=True, color='#1f7ced', bins=30)
plt.title('Distribution of User Age (Zoomed-In)', fontsize=16)
plt.xlabel('User Age', fontsize=14)
plt.ylabel('Frequency', fontsize=14)
plt.grid(axis='y', linestyle='--', alpha=0.7)
# Save the plot as a PNG file
plt.savefig('image/distribution_of_user_age_(zoomed-in).png', dpi=300, bbox_inches='tight')
plt.show()

citibike_trip_sample_df['user_age'].quantile(0.99), citibike_trip_sample_df['user_age'].quantile(0.999)

(67.0, 76.10700000001816)

print(citibike_trip_sample_df.query('user_age>=76').shape)
citibike_trip_sample_df.query('user_age>=76').head()

(339, 23)

print(citibike_trip_sample_df.gender.value_counts())
print('----------------')
print(citibike_trip_sample_df.usertype.value_counts())

gender
male       211692
female      64887
unknown     39206
Name: count, dtype: int64
----------------
usertype
Subscriber    277448
Customer       38337
Name: count, dtype: int64

# Bar chart for categorical variables: gender and usertype
fig, axes = plt.subplots(1, 2, figsize=(14, 6))  # Two charts side by side

# Bar chart for Gender
sns.countplot(data=citibike_trip_sample_df, x='gender', ax=axes[0], palette='Blues')
axes[0].set_title('Distribution of Gender', fontsize=14)
axes[0].set_xlabel('Gender', fontsize=12)
axes[0].set_ylabel('Count', fontsize=12)

# Bar chart for Usertype
sns.countplot(data=citibike_trip_sample_df, x='usertype', ax=axes[1], palette='Blues')
axes[1].set_title('Distribution of Usertype', fontsize=14)
axes[1].set_xlabel('Usertype', fontsize=12)
axes[1].set_ylabel('Count', fontsize=12)

plt.tight_layout()

# Save the figure as a PNG file
plt.savefig('image/distribution_of_usertype_and_gender_bar_charts.png', dpi=300, bbox_inches='tight')

# Check the quantile and percentile of the trip duration
q1 = citibike_trip_sample_df['tripduration'].quantile(0.01)
q99 = citibike_trip_sample_df['tripduration'].quantile(0.99)
percentile_24hr = percentileofscore(citibike_trip_sample_df['tripduration'], 24*60*60, kind='rank') / 100

# Printing the information
print(f"1st Percentile (0.01 Quantile) of 'tripduration': {q1}")
print(f"99th Percentile (0.99 Quantile) of 'tripduration': {q99}")
print(f"Percentile rank of 24 hours (in seconds) for 'tripduration': {percentile_24hr:.4f}")

1st Percentile (0.01 Quantile) of 'tripduration': 119.0
99th Percentile (0.99 Quantile) of 'tripduration': 3879.0
Percentile rank of 24 hours (in seconds) for 'tripduration': 0.9998

plt.figure(figsize=(10, 6))
sns.histplot(citibike_trip_sample_df[citibike_trip_sample_df['tripduration'] <
                                     citibike_trip_sample_df['tripduration'].quantile(0.99)],
             x='tripduration', kde=True, color='#1f7ced', bins=30)
plt.title('Distribution of Trip Duration (Zoomed-In)', fontsize=16)
plt.xlabel('Trip Duration (seconds)', fontsize=14)
plt.ylabel('Frequency', fontsize=14)
plt.grid(axis='y', linestyle='--', alpha=0.7)

plt.show()
# Save the figure as a PNG file
plt.savefig('image/distribution_of_trip_duration.png', dpi=300, bbox_inches='tight')

<Figure size 640x480 with 0 Axes>

# Boxplot by Usertype
plt.figure(figsize=(12, 6))
sns.boxplot(data=citibike_trip_sample_df, x='usertype', y='tripduration', color='skyblue', palette='Set2')
plt.title('Boxplot of Trip Duration by Usertype', fontsize=16)
plt.xlabel('Usertype', fontsize=14)
plt.ylabel('Trip Duration (seconds)', fontsize=14)
plt.ylim(0, citibike_trip_sample_df['tripduration'].quantile(0.99))  # Focus on 99th percentile
plt.grid(axis='y', linestyle='--', alpha=0.7)
# Save the figure as a PNG file
plt.savefig('image/boxplot_of_trip_duration_by_usertype.png', dpi=300, bbox_inches='tight')
plt.show()

# Boxplot by Gender
plt.figure(figsize=(12, 6))
sns.boxplot(data=citibike_trip_sample_df, x='gender', y='tripduration', color='skyblue', palette='Set2')
plt.title('Boxplot of Trip Duration by Gender', fontsize=16)
plt.xlabel('Gender', fontsize=14)
plt.ylabel('Trip Duration (seconds)', fontsize=14)
plt.ylim(0, citibike_trip_sample_df['tripduration'].quantile(0.99))  # Focus on 99th percentile
plt.grid(axis='y', linestyle='--', alpha=0.7)
# Save the figure as a PNG file
plt.savefig('image/boxplot_of_trip_duration_by_gender.png', dpi=300, bbox_inches='tight')
plt.show()

trip_start_hour_map_data = citibike_trip_sample_df.groupby(['start_station_name', 'start_station_latitude', 'start_station_longitude', 'start_hour']).size().reset_index(name='count')
trip_start_hour_map_data.columns = ['station_name', 'latitude', 'longitude', 'hour', 'count']
trip_end_hour_map_data = citibike_trip_sample_df.groupby(['end_station_name', 'end_station_latitude', 'end_station_longitude', 'stop_hour']).size().reset_index(name='count')
trip_end_hour_map_data.columns = ['station_name', 'latitude', 'longitude', 'hour', 'count']
trip_start_hour_map_data.shape, trip_end_hour_map_data.shape

((11536, 5), (11607, 5))

trip_start_month_map_data = citibike_trip_sample_df.groupby(['start_station_name', 'start_station_latitude', 'start_station_longitude', 'start_month']).size().reset_index(name='count')
trip_start_month_map_data.columns = ['station_name', 'latitude', 'longitude', 'month', 'count']
trip_end_month_map_data = citibike_trip_sample_df.groupby(['end_station_name', 'end_station_latitude', 'end_station_longitude', 'stop_month']).size().reset_index(name='count')
trip_end_month_map_data.columns = ['station_name', 'latitude', 'longitude', 'month', 'count']
trip_start_month_map_data.shape, trip_end_month_map_data.shape

((6052, 5), (6062, 5))

trip_start_hour_map_data.latitude.mean(), trip_start_hour_map_data.longitude.mean()

(40.73075455494539, -73.98023185396151)

# def draw_heatmap(df):
#   # Create a base map
#   m = folium.Map(location=[40.73, -73.98], zoom_start=12, width=800, height=800)
#   # Add heatmap to the map
#   heat_data = [[row['latitude'], row['longitude'], row['count']] for index, row in df.iterrows()]
#   HeatMap(heat_data, radius=15).add_to(m)
#   return m

# def draw_heatmap_with_time(df, timeframe_col = 'hour'):
#     # Ensure 'hour' is sorted for proper time sequencing
#     df = df.sort_values(by=timeframe_col)

#     # Group data by hour and prepare heatmap data
#     heat_data = []
#     for hour, group in df.groupby(timeframe_col):
#         heat_data.append(
#             [[row['latitude'], row['longitude'], row['count']] for _, row in group.iterrows()]
#         )

#     # Create a base map
#     m = folium.Map(location=[40.73, -73.98], zoom_start=12, width=800, height=800)

#     # Add the HeatMapWithTime layer
#     HeatMapWithTime(
#         data=heat_data,
#         radius=20,
#         index=list(df[timeframe_col].unique()),  # Use unique 'hour' values as the time index
#         auto_play=True,  # Automatically play through frames
#         max_opacity=0.5,  # Reduce the opacity for a softer visual effect
#         scale_radius=False,# Keep the radius consistent
#         # scale_radius=True,
#         #  gradient={0.1: 'blue', 0.3: 'cyan', 0.5: 'green', 0.75: 'yellow', 1: 'red'}

#     ).add_to(m)

#     return m

# display(draw_heatmap_with_time(trip_start_hour_map_data, 'hour'))

def draw_heatmap_with_time_plotly(df, title, timeframe_col='hour'):
    """
    Create an animated heatmap using Plotly to display usage over time with a constant scale.

    Parameters:
    - df: DataFrame with columns ['station_name', 'latitude', 'longitude', 'hour', 'count'].
    - timeframe_col: The column representing time (e.g., 'hour').

    Returns:
    - fig: A Plotly figure object.
    """
    # Sort by time for proper sequencing
    df = df.sort_values(by=timeframe_col)

    # Determine consistent size and color scales
    max_count = df['count'].max()

    # Create an animated scatter map
    fig = px.scatter_mapbox(
        df,
        lat='latitude',
        lon='longitude',
        size='count',
        color='count',
        animation_frame=timeframe_col,
        hover_name='station_name',
        size_max=15,  # Maximum size of the circle
        zoom=11,
        mapbox_style="carto-positron",
        title=title
    )

    # Set consistent size and color scales
    fig.update_traces(marker=dict(sizemin=3))  # Set a minimum circle size
    fig.update_layout(
        coloraxis_colorbar=dict(title="Usage Count"),
        coloraxis=dict(cmin=0, cmax=max_count),  # Constant color scale
        margin={"r": 0, "t": 50, "l": 0, "b": 0},
        width=900,  # Narrower width
        height=900  # Taller height
    )

    return fig

fig = draw_heatmap_with_time_plotly(trip_start_hour_map_data, 'Hourly Outflow of CitiBike Stations (Based on Start Stations)', 'hour')
fig.write_html("html/trip_start_hour_heatmap_animation.html")
fig.show()

fig = draw_heatmap_with_time_plotly(trip_end_hour_map_data, 'Hourly Inflow of CitiBike Stations (Based on End Stations)', 'hour')
fig.write_html("html/trip_stop_hour_heatmap_animation.html")
fig.show()

fig = draw_heatmap_with_time_plotly(trip_start_month_map_data, 'Monthly Outflow of CitiBike Stations (Based on Start Stations)', 'month')
fig.write_html("html/trip_start_month_heatmap_animation.html")
fig.show()

fig = draw_heatmap_with_time_plotly(trip_end_month_map_data, 'Monthly Inflow of CitiBike Stations (Based on End Stations)', 'month')
fig.write_html("html/trip_stop_month_heatmap_animation.html")
fig.show()

# Create a 'year_month' column
citibike_trip_sample_df['year_month'] = (
    citibike_trip_sample_df['start_year'].astype(str) + "-" +
    citibike_trip_sample_df['start_month'].astype(str).str.zfill(2)
)

# Group data by year_month and usertype
grouped_data = citibike_trip_sample_df.groupby(['year_month', 'usertype']).size().reset_index(name='num_trips')

# Pivot the data for plotting
pivot_data = grouped_data.pivot(index='year_month', columns='usertype', values='num_trips').fillna(0)

# Plot the data as separate bar plots for each user type
fig, ax = plt.subplots(figsize=(14, 8))
pivot_data.plot(kind='bar', ax=ax, color=['#1f77b4', '#ff7f0e'])

# Set plot details
ax.set_title('Number of Trips by Year-Month and User Type', fontsize=14)
ax.set_xlabel('Year-Month', fontsize=12)
ax.set_ylabel('Number of Trips', fontsize=12)
ax.legend(title='User Type', fontsize=10)
ax.grid(axis='y', linestyle='--', alpha=0.7)

# Rotate x-axis labels for better readability
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
# Save the figure as a PNG file
plt.savefig('image/number_of_trips_by_year-month_and_usertype.png', dpi=300, bbox_inches='tight')
plt.show()

import calendar
month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

# Map month numbers to abbreviated names
citibike_trip_sample_df['month_name'] = citibike_trip_sample_df['start_month'].map(
    lambda x: calendar.month_abbr[x]
)
monthly_trips = (
    citibike_trip_sample_df.groupby(['month_name', 'year_month', 'usertype'])
    .size()
    .reset_index(name='monthly_trips')
)

# Calculate the average number of trips per month for each year_month and usertype
avg_monthly_trips = (
    monthly_trips.groupby(['month_name', 'usertype'])['monthly_trips']
    .mean()
    .reset_index(name='avg_trips_per_month')
)

avg_monthly_trips['month_name'] = pd.Categorical(
    avg_monthly_trips['month_name'], categories=month_order, ordered=True
)
# Pivot the data for plotting
pivot_avg_monthly_trips = avg_monthly_trips.pivot(index='month_name', columns='usertype', values='avg_trips_per_month').fillna(0)

# Plot the average trips as a bar plot
fig, ax = plt.subplots(figsize=(14, 8))
pivot_avg_monthly_trips.plot(kind='bar', ax=ax, color=['#1f77b4', '#ff7f0e'])

# Customize the plot
ax.set_title('Average Number of Trips per Month by User Type', fontsize=14)
ax.set_xlabel('Month', fontsize=12)
ax.set_ylabel('Average Number of Trips per Month', fontsize=12)
ax.legend(title='User Type', fontsize=10)
ax.grid(axis='y', linestyle='--', alpha=0.7)

# Rotate x-axis labels for better readability
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
# Save the figure as a PNG file
plt.savefig('image/average_number_of_trips_per_month_by_usertype.png', dpi=300, bbox_inches='tight')
plt.show()

# Extract the day of the week
citibike_trip_sample_df['day_of_week'] = citibike_trip_sample_df['starttime'].dt.day_name()

# Extract the date part from 'starttime'
citibike_trip_sample_df['start_date'] = citibike_trip_sample_df['starttime'].dt.date
citibike_trip_sample_df['stop_date'] = citibike_trip_sample_df['stoptime'].dt.date

# Group by day_of_week, usertype, and start_date to calculate the number of trips per day
daily_trips = (
    citibike_trip_sample_df.groupby(['day_of_week', 'usertype', 'start_date'])
    .size()
    .reset_index(name='daily_trips')
)

# Calculate the average number of trips per day for each day_of_week and usertype
avg_trips = (
    daily_trips.groupby(['day_of_week', 'usertype'])['daily_trips']
    .mean()
    .reset_index(name='avg_trips_per_day')
)

# Ensure the days are in the correct order
day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
avg_trips['day_of_week'] = pd.Categorical(avg_trips['day_of_week'], categories=day_order, ordered=True)

# Pivot the data for plotting
pivot_avg_trips = avg_trips.pivot(index='day_of_week', columns='usertype', values='avg_trips_per_day').fillna(0)

# Plot the average trips as a bar plot
fig, ax = plt.subplots(figsize=(14, 8))
pivot_avg_trips.plot(kind='bar', ax=ax, color=['#1f77b4', '#ff7f0e'])

# Customize the plot
ax.set_title('Average Number of Trips per Day of Week by User Type', fontsize=14)
ax.set_xlabel('Day of Week', fontsize=12)
ax.set_ylabel('Average Number of Trips per Day', fontsize=12)
ax.legend(title='User Type', fontsize=10)
ax.grid(axis='y', linestyle='--', alpha=0.7)

# Rotate x-axis labels for better readability
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
# Save the figure as a PNG file
plt.savefig('image/average_number_of_trips_per_dayofweek_by_usertype.png', dpi=300, bbox_inches='tight')
plt.show()

# Remove outliers
citibike_trip_sample_clean_df = citibike_trip_sample_df.copy()
print(citibike_trip_sample_df.shape)
citibike_trip_sample_clean_df = citibike_trip_sample_clean_df.query('starttime<stoptime')
print(citibike_trip_sample_clean_df.shape)
citibike_trip_sample_clean_df = citibike_trip_sample_clean_df.query('tripduration < 24*60*60')
print(citibike_trip_sample_clean_df.shape)
## Most customers don't put birth year when rent a bike. And this is not the most essential info when it comes to usage forecast
# citibike_trip_sample_clean_df = citibike_trip_sample_clean_df.query('user_age < 76')
# print(citibike_trip_sample_clean_df.shape)

(315785, 28)
(315784, 28)
(315712, 28)

citibike_trip_sample_df.head()

def usage_forecast(df, output_filename):
  daily_trips = df.groupby(df['starttime'].dt.to_period('D')).size()
  daily_trips.index = daily_trips.index.to_timestamp()  # Convert to DatetimeIndex


  daily_trips = daily_trips.reset_index()
  # Ensure time series has a continuous index (fill missing days with 0 trips)
  # daily_trips = daily_trips.asfreq('D', fill_value=0)
  daily_trips.columns = ['ds', 'y']  # Rename columns for Prophet compatibility

  # Split data for training and testing
  train = daily_trips[(daily_trips['ds'] >= '2013-07-01') & (daily_trips['ds'] <= '2016-06-30')]
  test = daily_trips[(daily_trips['ds'] >= '2016-07-01') & (daily_trips['ds'] <= '2016-08-30')]

  # Initialize and train the Prophet model
  model = Prophet(interval_width=0.95) # Set to 95% confidence interval (default value is 80%)
  model.add_seasonality(name='weekly', period=7, fourier_order=3)
  model.add_seasonality(name='yearly', period=365.25, fourier_order=5)
  model.fit(train)

  # Make predictions for the test period
  future = model.make_future_dataframe(periods=365, freq='D')
  forecast = model.predict(future)

  # Extract predictions for the test period
  forecast_test = forecast[forecast['ds'].isin(test['ds'])]

  # Calculate evaluation metrics
  y_true = test['y'].values
  y_pred = forecast_test['yhat'].values

  # Mean Absolute Error (MAE)
  mae = mean_absolute_error(y_true, y_pred)

  # Mean Absolute Percentage Error (MAPE)
  mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100

  # Root Mean Squared Error (RMSE)
  rmse = mean_squared_error(y_true, y_pred, squared=False)

  # Root Mean Squared Percentage Error (RMSPE)
  rmspe = np.sqrt(np.mean(((y_true - y_pred) / y_true) ** 2)) * 100

  # Print metrics
  print(f"Mean Absolute Error (MAE): {mae:.2f}")
  print(f"Mean Absolute Percentage Error (MAPE): {mape:.2f}%")
  print(f"Root Mean Squared Error (RMSE): {rmse:.2f}")
  print(f"Root Mean Squared Percentage Error (RMSPE): {rmspe:.2f}%")

 # Create side-by-side plots
  fig, axes = plt.subplots(1, 2, figsize=(18, 6))

  # Full Forecast Plot
  axes[0].set_title('Prophet Forecast: Daily Trips')
  fig_full = model.plot(forecast, ax=axes[0])
  axes[0].scatter(test['ds'], test['y'], color='orange', label='Test Data', zorder=3, s=10)  # Add test data points
  axes[0].set_xlabel('Date')
  axes[0].set_ylabel('Number of Trips')
  axes[0].legend()

  # Zoomed-In Forecast Plot
  axes[1].set_title('Prophet Forecast: Zoomed-In for Last Two Months')
  fig_zoom = model.plot(forecast, ax=axes[1])
  axes[1].plot(
      test['ds'], test['y'],
      color='orange',
      label='Test Data (Line)',
      zorder=2,
      linestyle='-',
      marker='o',
      linewidth=1  # Adjust line thickness
  )
  axes[1].set_xlim(pd.Timestamp('2016-07-01'), pd.Timestamp('2016-08-30'))
  axes[1].set_xlabel('Date')
  axes[1].set_ylabel('Number of Trips')
  axes[1].legend()

  # Adjust layout
  plt.tight_layout()
  plt.savefig(output_filename, dpi=300, bbox_inches='tight')
  plt.show()

usage_forecast(citibike_trip_sample_clean_df, "image/prediction_for_citibike_user_usage.png")

INFO:prophet:Found custom seasonality named 'yearly', disabling built-in 'yearly' seasonality.
INFO:prophet:Found custom seasonality named 'weekly', disabling built-in 'weekly' seasonality.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmpk1t6jpc7/s5_49kqn.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmpk1t6jpc7/2yp_0let.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=93518', 'data', 'file=/tmp/tmpk1t6jpc7/s5_49kqn.json', 'init=/tmp/tmpk1t6jpc7/2yp_0let.json', 'output', 'file=/tmp/tmpk1t6jpc7/prophet_modelbkfldulp/prophet_model-20241204204053.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']
20:40:53 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
20:40:53 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing

Mean Absolute Error (MAE): 50.96
Mean Absolute Percentage Error (MAPE): 12.67%
Root Mean Squared Error (RMSE): 67.11
Root Mean Squared Percentage Error (RMSPE): 19.09%

# Aggregate daily data
subscriber_citibike_trip_sample_clean_df = citibike_trip_sample_clean_df.query('usertype=="Subscriber"')
customer_citibike_trip_sample_clean_df = citibike_trip_sample_clean_df.query('usertype=="Customer"')

usage_forecast(subscriber_citibike_trip_sample_clean_df, "image/prediction_for_citibike_subscriber_usage.png")

INFO:prophet:Found custom seasonality named 'yearly', disabling built-in 'yearly' seasonality.
INFO:prophet:Found custom seasonality named 'weekly', disabling built-in 'weekly' seasonality.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmpk1t6jpc7/17qe_fqt.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmpk1t6jpc7/lst7g6hf.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=15292', 'data', 'file=/tmp/tmpk1t6jpc7/17qe_fqt.json', 'init=/tmp/tmpk1t6jpc7/lst7g6hf.json', 'output', 'file=/tmp/tmpk1t6jpc7/prophet_modeliiz6uylb/prophet_model-20241204215056.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']
21:50:56 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
21:50:56 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing

Mean Absolute Error (MAE): 53.27
Mean Absolute Percentage Error (MAPE): 17.06%
Root Mean Squared Error (RMSE): 69.65
Root Mean Squared Percentage Error (RMSPE): 26.53%

usage_forecast(customer_citibike_trip_sample_clean_df, "image/prediction_for_citibike_customer_usage.png")

INFO:prophet:Found custom seasonality named 'yearly', disabling built-in 'yearly' seasonality.
INFO:prophet:Found custom seasonality named 'weekly', disabling built-in 'weekly' seasonality.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmpk1t6jpc7/y7vd3_j4.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmpk1t6jpc7/hd70wvgm.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=94701', 'data', 'file=/tmp/tmpk1t6jpc7/y7vd3_j4.json', 'init=/tmp/tmpk1t6jpc7/hd70wvgm.json', 'output', 'file=/tmp/tmpk1t6jpc7/prophet_model3qmx3aza/prophet_model-20241204204402.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']
20:44:02 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
20:44:02 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing

Mean Absolute Error (MAE): 17.33
Mean Absolute Percentage Error (MAPE): 28.24%
Root Mean Squared Error (RMSE): 22.23
Root Mean Squared Percentage Error (RMSPE): 36.40%

df = citibike_trip_sample_clean_df.copy()

# Calculate the usage for each station
start_usage = df.groupby(['start_station_id', 'start_station_name', 'start_station_latitude', 'start_station_longitude']).size().reset_index(name='start_count')
end_usage = df.groupby(['end_station_id', 'end_station_name', 'end_station_latitude', 'end_station_longitude']).size().reset_index(name='end_count')

# Merge start and end usage to calculate total usage for each station
station_usage = pd.merge(start_usage, end_usage,
                         left_on='start_station_id', right_on='end_station_id',
                         how='outer')

# Fill NaN values with 0 and calculate total usage
station_usage.fillna({'start_count': 0, 'end_count': 0}, inplace=True)
station_usage['total_usage'] = station_usage['start_count'] + station_usage['end_count']

# Merge latitude and longitude information
station_usage['latitude'] = station_usage['start_station_latitude'].where(
    ~station_usage['start_station_latitude'].isnull(), station_usage['end_station_latitude'])
station_usage['longitude'] = station_usage['start_station_longitude'].where(
    ~station_usage['start_station_longitude'].isnull(), station_usage['end_station_longitude'])

# Use `start_station_name` if available; otherwise, fall back to `end_station_name`
station_usage['station_name'] = station_usage['start_station_name'].where(
    station_usage['start_station_name'] != '', station_usage['end_station_name'])

# Sort by total usage and get the top 10 most used stations
top_stations = station_usage[['station_name', 'total_usage']].sort_values(by='total_usage', ascending=True).tail(10)

# Draw the horizontal bar chart
plt.figure(figsize=(10, 6))
plt.barh(top_stations['station_name'], top_stations['total_usage'])
plt.title('Top 10 Most Used Stations')
plt.xlabel('Total Usage')
plt.ylabel('Station Name')
plt.tight_layout()
# Save the figure as a PNG file
plt.savefig('image/top_10_most_used_stations.png', dpi=300, bbox_inches='tight')
plt.show()

import plotly.express as px

# Get the top 100 most used stations
top_100_stations = station_usage[['station_name', 'latitude', 'longitude', 'total_usage']].sort_values(by='total_usage', ascending=False).head(100)

# Plot the top 100 stations on the map using Plotly
fig = px.scatter_mapbox(
    top_100_stations,
    lat="latitude",
    lon="longitude",
    size="total_usage",
    color="total_usage",
    hover_name="station_name",
    size_max=15,
    zoom=12,
    mapbox_style="carto-positron",
    title="Top 100 Most Used Citibike Stations"
)
fig.update_layout(
        width=900,  # Narrower width
        height=900  # Taller height
    )
fig.write_html("html/top_100_most_used_stations.html")
fig.show()

from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
# Add bike_in and bike_out columns
df['bike_out'] = -1  # Bikes leaving the station
df['bike_in'] = 1    # Bikes arriving at the station

# Calculate bike outflows (grouped by start hour)
outflow = df.groupby(['start_station_id', 'start_hour'])['bike_out'].sum().reset_index()

# Calculate bike inflows (grouped by stop hour)
inflow = df.groupby(['end_station_id', 'stop_hour'])['bike_in'].sum().reset_index()

# Rename columns for consistency
outflow.rename(columns={'start_station_id': 'station_id', 'start_hour': 'hour'}, inplace=True)
inflow.rename(columns={'end_station_id': 'station_id', 'stop_hour': 'hour'}, inplace=True)

# Combine inflow and outflow into a single DataFrame
bike_flow = pd.concat([outflow, inflow], axis=0, ignore_index=True)

# Group by station and hour to calculate net bike flow (inflow - outflow)
bike_flow = bike_flow.groupby(['station_id', 'hour']).sum().reset_index()
bike_flow['net_flow'] = bike_flow['bike_in'] + bike_flow['bike_out']

# Prepare data for clustering: pivot to have stations as rows and net flow as columns
time_series_matrix = bike_flow.pivot_table(
    index='station_id',
    columns='hour',
    values='net_flow',
    fill_value=0
)

# Standardize the data for clustering
scaler = StandardScaler()
time_series_scaled = scaler.fit_transform(time_series_matrix)

bike_flow.head()

time_series_matrix

# Determine optimal number of clusters using silhouette analysis
silhouette_scores = []
k_range = range(2, 10)

for k in k_range:
    kmeans = KMeans(n_clusters=k, random_state=42)
    cluster_labels = kmeans.fit_predict(time_series_scaled)
    score = silhouette_score(time_series_scaled, cluster_labels)
    silhouette_scores.append(score)

# Plot the silhouette scores
plt.figure(figsize=(10, 6))
plt.plot(k_range, silhouette_scores, marker='o')
plt.title('Silhouette Scores for K-Means Clustering')
plt.xlabel('Number of Clusters')
plt.ylabel('Silhouette Score')
plt.show()

silhouette_score_optimal_k = k_range[np.argmax(silhouette_scores)]
print(f"Optimal number of clusters based on silhouette score: {silhouette_score_optimal_k}")

Optimal number of clusters based on silhouette score: 2

def elbow_method(data, k_range):
    """
    Determine the optimal number of clusters using the elbow method.

    Parameters:
    - data: The dataset to cluster (e.g., scaled time-series matrix).
    - k_range: A range of possible cluster numbers to evaluate.

    Returns:
    - inertia_scores: A list of inertia scores for each value of k.
    """
    inertia_scores = []

    # Compute KMeans for each value of k
    for k in k_range:
        kmeans = KMeans(n_clusters=k, random_state=42)
        kmeans.fit(data)
        inertia_scores.append(kmeans.inertia_)  # Append the inertia (sum of squared distances)

    # Plot the inertia scores against k
    plt.figure(figsize=(10, 6))
    plt.plot(k_range, inertia_scores, marker='o')
    plt.title('Elbow Method to Determine Optimal k')
    plt.xlabel('Number of Clusters (k)')
    plt.ylabel('Inertia (Sum of Squared Distances)')
    plt.grid()
    plt.show()

    return inertia_scores

# Example usage
k_range = range(1, 11)  # Test k from 1 to 10
inertia_scores = elbow_method(time_series_scaled, k_range)

# Choose optimal number of clusters (based on silhouette score or elbow method)

elbow_method_optimal_k = 3
print(f"Optimal number of clusters based on elbow method: {elbow_method_optimal_k}")

Optimal number of clusters based on elbow method: 3

# Perform clustering with the optimal number of clusters
kmeans = KMeans(n_clusters=elbow_method_optimal_k, random_state=42)
station_clusters = kmeans.fit_predict(time_series_scaled)

# Add cluster labels to stations
clustered_stations = pd.DataFrame({
    'station_id': time_series_matrix.index,
    'cluster': station_clusters
})

Optimal number of clusters based on elbow method: 3

clustered_stations.cluster.value_counts()

# Add cluster labels to the scaled data for visualization
clustered_data = pd.DataFrame(time_series_scaled, index=time_series_matrix.index)
clustered_data['cluster'] = station_clusters

# Plot the average pattern and all others for each cluster
for cluster_id in sorted(clustered_data['cluster'].unique()):
    cluster_members = clustered_data[clustered_data['cluster'] == cluster_id].drop(columns=['cluster'])

    # Calculate the average pattern for the cluster
    cluster_average = cluster_members.mean(axis=0)

    plt.figure(figsize=(10, 6))

    # Plot all members in grey
    for _, member in cluster_members.iterrows():
        plt.plot(member.values, color='grey', alpha=0.3)

    # Plot the average in red
    plt.plot(cluster_average.values, color='red', label='Cluster Average', linewidth=2)

    # Add labels and title
    plt.title(f'Typical Shape for Cluster {cluster_id}')
    plt.xlabel('Hour')
    plt.ylabel('Scaled Net Bike Flow')
    plt.legend()
    plt.tight_layout()
    # Save the figure as a PNG file
    plt.savefig('image/typical_shape_for_cluster_{}.png'.format(cluster_id), dpi=300, bbox_inches='tight')
    plt.show()

	tripduration	starttime	stoptime	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bikeid	usertype	birth_year	gender	customer_plan	start_hour	start_month	start_year	stop_hour	stop_month	stop_year	user_age
0	2319	2016-03-09 13:08:00	2016-03-09 13:47:00	520	W 52 St & 5 Ave	40.759923	-73.976485	363	West Thames St	40.708347	-74.017134	23062	Subscriber	1972.0	male	NaN	13	3	2016	13	3	2016	44.0
1	313	2015-07-09 15:42:00	2015-07-09 15:47:00	520	W 52 St & 5 Ave	40.759923	-73.976485	493	W 45 St & 6 Ave	40.756800	-73.982912	16909	Subscriber	1968.0	female	NaN	15	7	2015	15	7	2015	47.0
2	906	2016-01-11 18:32:00	2016-01-11 18:47:00	520	W 52 St & 5 Ave	40.759923	-73.976485	3162	W 78 St & Broadway	40.783400	-73.980931	15614	Subscriber	1961.0	male	NaN	18	1	2016	18	1	2016	55.0
3	716	2013-10-30 11:53:00	2013-10-30 12:05:00	520	W 52 St & 5 Ave	40.759923	-73.976485	533	Broadway & W 39 St	40.752996	-73.987216	19280	Subscriber	1954.0	male	NaN	11	10	2013	12	10	2013	59.0
4	312	2014-06-04 16:12:00	2014-06-04 16:17:00	520	W 52 St & 5 Ave	40.759923	-73.976485	519	E 42 St & Vanderbilt Ave	40.752416	-73.978370	16483	Subscriber	1963.0	male	NaN	16	6	2014	16	6	2014	51.0

	tripduration	starttime	stoptime	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bikeid	usertype	birth_year	gender	customer_plan	start_hour	start_month	start_year	stop_hour	stop_month	stop_year	user_age
1636	1305	2014-10-18 08:25:00	2014-10-18 08:47:00	349	Rivington St & Ridge St	40.718502	-73.983299	467	Dean St & 4 Ave	40.683125	-73.978951	17955	Subscriber	1910.0	male	NaN	8	10	2014	8	10	2014	104.0
3121	286	2016-07-28 18:41:00	2016-07-28 18:46:00	395	Bond St & Schermerhorn St	40.688070	-73.984106	3246	Montague St & Clinton St	40.694281	-73.992300	25540	Subscriber	1940.0	male	NaN	18	7	2016	18	7	2016	76.0
3249	1411	2016-02-22 09:02:00	2016-02-22 09:25:00	446	W 24 St & 7 Ave	40.744876	-73.995299	475	E 16 St & Irving Pl	40.735243	-73.987586	22987	Subscriber	1940.0	female	NaN	9	2	2016	9	2	2016	76.0
5975	976	2016-01-11 16:29:00	2016-01-11 16:45:00	3141	1 Ave & E 68 St	40.765005	-73.958185	3171	Amsterdam Ave & W 82 St	40.785247	-73.976673	20933	Subscriber	1939.0	male	NaN	16	1	2016	16	1	2016	77.0
7346	2327	2014-11-10 13:37:00	2014-11-10 14:16:00	408	Market St & Cherry St	40.710762	-73.994004	302	Avenue D & E 3 St	40.720828	-73.977932	19900	Subscriber	1936.0	male	NaN	13	11	2014	14	11	2014	78.0

	tripduration	starttime	stoptime	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bikeid	usertype	birth_year	gender	customer_plan	start_hour	start_month	start_year	stop_hour	stop_month	stop_year	user_age	year_month	month_name	day_of_week	start_date	stop_date
0	2319	2016-03-09 13:08:00	2016-03-09 13:47:00	520	W 52 St & 5 Ave	40.759923	-73.976485	363	West Thames St	40.708347	-74.017134	23062	Subscriber	1972.0	male	NaN	13	3	2016	13	3	2016	44.0	2016-03	Mar	Wednesday	2016-03-09	2016-03-09
1	313	2015-07-09 15:42:00	2015-07-09 15:47:00	520	W 52 St & 5 Ave	40.759923	-73.976485	493	W 45 St & 6 Ave	40.756800	-73.982912	16909	Subscriber	1968.0	female	NaN	15	7	2015	15	7	2015	47.0	2015-07	Jul	Thursday	2015-07-09	2015-07-09
2	906	2016-01-11 18:32:00	2016-01-11 18:47:00	520	W 52 St & 5 Ave	40.759923	-73.976485	3162	W 78 St & Broadway	40.783400	-73.980931	15614	Subscriber	1961.0	male	NaN	18	1	2016	18	1	2016	55.0	2016-01	Jan	Monday	2016-01-11	2016-01-11
3	716	2013-10-30 11:53:00	2013-10-30 12:05:00	520	W 52 St & 5 Ave	40.759923	-73.976485	533	Broadway & W 39 St	40.752996	-73.987216	19280	Subscriber	1954.0	male	NaN	11	10	2013	12	10	2013	59.0	2013-10	Oct	Wednesday	2013-10-30	2013-10-30
4	312	2014-06-04 16:12:00	2014-06-04 16:17:00	520	W 52 St & 5 Ave	40.759923	-73.976485	519	E 42 St & Vanderbilt Ave	40.752416	-73.978370	16483	Subscriber	1963.0	male	NaN	16	6	2014	16	6	2014	51.0	2014-06	Jun	Wednesday	2014-06-04	2014-06-04

hour	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23
station_id
116	1.0	4.0	3.0	0.0	-4.0	-7.0	6.0	7.0	2.0	39.0	10.0	1.0	9.0	12.0	-22.0	-9.0	-32.0	-30.0	-7.0	-8.0	3.0	20.0	17.0	-1.0
119	2.0	-1.0	1.0	0.0	0.0	0.0	-6.0	-3.0	0.0	-5.0	-5.0	-3.0	-3.0	0.0	1.0	1.0	1.0	3.0	9.0	-5.0	3.0	5.0	1.0	3.0
120	2.0	2.0	0.0	1.0	-1.0	-2.0	-4.0	-11.0	-26.0	-19.0	-3.0	-3.0	-3.0	-5.0	5.0	0.0	-2.0	-3.0	7.0	10.0	13.0	3.0	7.0	6.0
127	-8.0	0.0	1.0	2.0	2.0	-7.0	-10.0	-33.0	-31.0	65.0	4.0	-28.0	-8.0	-13.0	11.0	-4.0	-1.0	27.0	13.0	16.0	-6.0	3.0	9.0	-4.0
128	5.0	0.0	-6.0	1.0	3.0	-9.0	0.0	14.0	58.0	33.0	4.0	-3.0	8.0	13.0	-27.0	9.0	-7.0	-22.0	-37.0	-31.0	22.0	10.0	9.0	5.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
546	18.0	3.0	2.0	-2.0	1.0	20.0	-11.0	7.0	40.0	49.0	9.0	-13.0	-9.0	6.0	-7.0	-19.0	-41.0	-56.0	-36.0	1.0	13.0	6.0	2.0	-3.0
72	3.0	-2.0	3.0	0.0	2.0	8.0	1.0	0.0	-72.0	-5.0	-7.0	-9.0	34.0	-6.0	-11.0	-7.0	2.0	6.0	17.0	-4.0	16.0	26.0	6.0	7.0
79	-3.0	1.0	-2.0	-2.0	3.0	1.0	-1.0	-1.0	34.0	57.0	35.0	11.0	-8.0	18.0	-6.0	7.0	-9.0	-20.0	-29.0	-4.0	-7.0	4.0	-8.0	-7.0
82	8.0	2.0	1.0	1.0	3.0	1.0	-5.0	-5.0	-18.0	-5.0	-10.0	0.0	4.0	-1.0	12.0	-2.0	-12.0	3.0	18.0	5.0	11.0	7.0	-2.0	11.0
83	-5.0	1.0	0.0	1.0	0.0	0.0	-8.0	-4.0	6.0	-6.0	-1.0	-2.0	0.0	4.0	8.0	-7.0	14.0	3.0	13.0	8.0	2.0	-2.0	-13.0	-3.0

Citibike Data Case Study¶

1. EDA¶

1.1 Import Data¶

1.1.1 Load citibike trip sample¶

1.1.1 Load citibike stations data¶

1.2 Explore Data Distributions and Identify Potential Outliers/Anomalies¶

1.2.1 Start Time and Stop Time¶

1.2.2 User Age¶

1.2.3 Gender and User Type Distribution¶

1.2.4 Trip Duration¶

1.2.5 Geolocation Distribution of Citibike Station in Use Over Time¶

1.2.6 Number of Trips By Month and By Day of Week¶

2. Citibike Usage Forecast¶

2.1 Remove Outliers¶

2.2 Citibike Daily Usage Forecast with All Usertypes¶

2.2.1 Time Series Decompostion and Forecast¶

1. Trend Component¶

2. Seasonality Component¶

3. Combined Model¶

Modeling Residuals and Prediction Intervals¶

Key Takeaways¶

2.2.2 Interpretation and Comparison of Prediction Performance Metrics¶

1. General Observations¶

2. Why is MAPE and RMSPE Lower for the Overall User Group?¶

3. Potential Improvements¶

4. Understanding Variance in Residuals¶

Key Takeaways¶

3. Bike Station Insights¶

3.1 Find Out the Top Used Bike Stations¶

3.2 Citibike Station Usage Pattern Clustering¶

3.2.1 Kmean Clustering¶

3.2.2 Cluster Selection Methods and Justification for ( k = 3 )¶

2. Silhouette Method¶

3. Why Choose ( k = 3 )?¶

Conclusion¶

3.2.3 Interpretation of the Citybike Station Usage Patterns in Each Cluster¶

	Type	Count	Mean	Median	Std	Min	25%	50%	75%	Max	Unique	Top	Freq
tripduration	Continuous	315785	938.498763	631.0	6534.949983	60	391.0	631.0	1051.0	1733173	NaN	NaN	NaN
start_station_id	Categorical	315785	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	575	519	3374
start_station_name	Categorical	315785	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	599	8 Ave & W 31 St	2812
start_station_latitude	Continuous	315785	40.735735	40.736529	0.020822	40.646678	40.721101	40.736529	40.750977	40.804213	NaN	NaN	NaN
start_station_longitude	Continuous	315785	-73.989392	-73.990093	0.014088	-74.017134	-73.999318	-73.990093	-73.981281	-73.929891	NaN	NaN	NaN
end_station_id	Categorical	315785	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	584	519	3028
end_station_name	Categorical	315785	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	607	E 17 St & Broadway	3012
end_station_latitude	Continuous	315785	40.735366	40.736502	0.075412	0.0	40.720874	40.736502	40.750664	40.804213	NaN	NaN	NaN
end_station_longitude	Continuous	315785	-73.989326	-73.990214	0.13242	-74.017134	-73.999744	-73.990214	-73.981346	0.0	NaN	NaN	NaN
bikeid	Continuous	315785	19074.89687	18779.0	2888.180823	14529	16687.0	18779.0	21090.0	26884	NaN	NaN	NaN
usertype	Categorical	315785	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	Subscriber	277448
birth_year	Continuous	276894	1976.528812	1979.0	11.449689	1885.0	1969.0	1979.0	1986.0	2000.0	NaN	NaN	NaN
gender	Categorical	315785	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	male	211692
customer_plan	Continuous	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
start_hour	Continuous	315785	13.969216	15.0	4.881489	0	10.0	15.0	18.0	23	NaN	NaN	NaN
start_month	Continuous	315785	7.100112	7.0	2.80401	1	5.0	7.0	9.0	12	NaN	NaN	NaN
start_year	Continuous	315785	2014.693956	2015.0	1.036277	2013	2014.0	2015.0	2016.0	2016	NaN	NaN	NaN
stop_hour	Continuous	315785	14.129157	15.0	4.927257	0	10.0	15.0	18.0	23	NaN	NaN	NaN
stop_month	Continuous	315785	7.100157	7.0	2.804024	1	5.0	7.0	9.0	12	NaN	NaN	NaN
stop_year	Continuous	315785	2014.693966	2015.0	1.036278	2013	2014.0	2015.0	2016.0	2016	NaN	NaN	NaN
user_age	Continuous	276894	38.160982	36.0	11.439389	16.0	29.0	36.0	46.0	131.0	NaN	NaN	NaN

	station_id_int	name	short_name	latitude	longitude	region_id	rental_methods	capacity	eightd_has_key_dispenser	num_bikes_available	num_bikes_disabled	num_docks_available	num_docks_disabled	is_installed	is_renting	is_returning	eightd_has_available_keys
0	3664	North Moore St & Greenwich St	5470.12	40.720195	-74.010301	71	KEY, CREDITCARD	29	False	2	1	0	29	True	True	True	False
1	4682	W 34 St & Hudson Blvd E	6535.04	40.755167	-74.000599	71	KEY, CREDITCARD	57	False	2	1	52	2	True	True	True	False
2	3233	E 48 St & 5 Ave	6626.01	40.757246	-73.978059	71	KEY, CREDITCARD	62	False	2	1	43	14	True	True	True	False
3	244	Willoughby Ave & Hall St	4611.03	40.691960	-73.965369	71	KEY, CREDITCARD	51	False	12	1	32	6	True	True	True	False
4	525	W 34 St & 11 Ave	6578.01	40.755942	-74.002116	71	KEY, CREDITCARD	80	False	35	1	22	19	True	True	True	False

	Type	Count	Unique	Top	Freq	Mean	Median	Std	Min	25%	50%	75%	Max
station_id_int	Categorical	1802	1802	116	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
name	Categorical	1802	1802	1 Ave & E 110 St	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
short_name	Categorical	1802	1802	2733.03	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
latitude	Continuous	1802	NaN	NaN	NaN	40.723477	40.740629	0.961808	0.0	40.696093	40.740629	40.793693	40.88226
longitude	Continuous	1802	NaN	NaN	NaN	-73.909348	-73.947438	1.742434	-74.03281	-73.982605	-73.947438	-73.918636	0.0
region_id	Categorical	1802	3	71	1800	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
rental_methods	Categorical	1802	1	KEY, CREDITCARD	1802	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
capacity	Continuous	1802	NaN	NaN	NaN	30.95616	25.0	17.046749	0	21.0	25.0	37.0	123
eightd_has_key_dispenser	Categorical	1802	1	False	1802	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
num_bikes_available	Continuous	1802	NaN	NaN	NaN	11.933962	7.0	13.884131	0	2.0	7.0	18.0	80
num_bikes_disabled	Continuous	1802	NaN	NaN	NaN	1.468923	1.0	1.718818	0	0.0	1.0	2.0	19
num_docks_available	Continuous	1802	NaN	NaN	NaN	16.583241	16.0	13.525389	0	7.0	16.0	21.0	105
num_docks_disabled	Continuous	1802	NaN	NaN	NaN	0.082686	0.0	1.063941	0	0.0	0.0	0.0	29
is_installed	Categorical	1802	2	True	1737	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
is_renting	Categorical	1802	2	True	1736	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
is_returning	Categorical	1802	2	True	1736	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
eightd_has_available_keys	Categorical	1802	1	False	1802	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	station_id	hour	bike_out	bike_in	net_flow
0	116	0	-15.0	16.0	1.0
1	116	1	-10.0	14.0	4.0
2	116	2	-9.0	12.0	3.0
3	116	3	-3.0	3.0	0.0
4	116	4	-7.0	3.0	-4.0

	count
cluster
2	435
0	91
1	66